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1. Non-malleability Against Polynomial Tampering

   Ball, Marshall; Chattopadhyay, Eshan; Liao, Jyun-Jie; Malkin, Tal; Tan, Li-Yang. (July 2020, CRYPTO)

   Micciancio, Daniele; Ristenpart, Thomas. (Ed.)

   We present the first explicit construction of a non-malleable code that can handle tampering functions that are bounded-degree polynomials. Prior to our work, this was only known for degree-1 polynomials (affine tampering functions), due to Chattopad- hyay and Li (STOC 2017). As a direct corollary, we obtain an explicit non-malleable code that is secure against tampering by bounded-size arithmetic circuits. We show applications of our non-malleable code in constructing non-malleable se- cret sharing schemes that are robust against bounded-degree polynomial tampering. In fact our result is stronger: we can handle adversaries that can adaptively choose the polynomial tampering function based on initial leakage of a bounded number of shares. Our results are derived from explicit constructions of seedless non-malleable ex- tractors that can handle bounded-degree polynomial tampering functions. Prior to our work, no such result was known even for degree-2 (quadratic) polynomials. 

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2. Global, Passive Detection of Connection Tampering

   https://doi.org/10.1145/3603269.3604875  

   Sundara_Raman, Ram; Merino, Louis-Henri; Bock, Kevin; Fayed, Marwan; Levin, Dave; Sullivan, Nick; Valenta, Luke (September 2023, ACM)
3. Medical Image Tampering Detection: a New Dataset and Baseline

   Reichman, B; Jing, L; Akin, O; Tian, Y. (December 2020, International Workshop on Artificial Intelligence for Healthcare Applications (AIHA), 2020.)

   null (Ed.)

   The recent advances in algorithmic photo-editing and the vulnerability of hospitals to cyberattacks raises the concern about the tampering of medical images. This paper introduces a new large scale dataset of tampered Computed Tomography (CT) scans generated by different methods, LuNoTim-CT dataset, which can serve as the most comprehensive testbed for comparative studies of data security in healthcare. We further propose a deep learning-based framework, ConnectionNet, to automatically detect if a medical image is tampered. The proposed ConnectionNet is able to handle small tampered regions and achieves promising results and can be used as the baseline for studies of medical image tampering detection. 

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4. Quantifying Nations’ Exposure to Traffic Observation and Selective Tampering

   https://doi.org/10.1007/978-3-030-98785-5_29  

   Gamero-Garrido, A.; Carisimo, E.; Hao, S.; Huffaker, B.; Snoeren, A.C.; Dainotti, A. (March 2022, PAM 2022: Passive and Active Measurement)
5. Self-powered Sensing and Time-Stamping of Tampering Events

   Mehta, Darshit; Zhou, Liang; Aono, Kenji; Chakrabartty, Shantanu (January 2018, Proceedings of 61st IEEE International Midwest Symposium on Circuits and Systems)